get_analysis_calibration

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden, and it does disclose a meaningful non-obvious behavior: only target_before_stop is positive, stop_before_target is negative, and ambiguous/incomplete/cancelled/neutral/unevaluated entries are exclusions. It does not cover side effects, permissions, or output format, but the most important behavioral rule is stated clearly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The action and scope are front-loaded, and the essential labeling/exclusion policy immediately follows. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 3 optional parameters, no output schema, and no annotations, so the description alone must enable correct invocation and interpretation. It explains labeling and exclusions but omits parameter meanings, the shape of the calibration result, and when to choose this over performance/audit siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description never mentions bias, bins, or symbol. The schema only provides types, an enum, and a pattern; the agent must guess what 'bias' filters, how 'bins' affects calibration, and what 'symbol' selects.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('Calculate') and a specific resource ('confidence calibration from the local analysis journal'), and it adds the positive/negative labeling and exclusion policy. However, it does not explicitly differentiate from siblings like get_analysis_performance or the audit/study tools, so the distinction must be inferred.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The intended use is implied: use this when you need confidence calibration from the local analysis journal. But there is no explicit when-not-to-use guidance, no prerequisite mention, and no comparison against the many research/audit sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.